# AI-BASED Tool to Estimate Sodium Intake in STAGE 3 to 5 CKD Patients—The UniverSel Study

## TL;DR

This study developed an AI tool to estimate sodium intake in CKD patients using urine data and clinical variables, aiming to improve personalized management.

## Contribution

The novel contribution is an AI-based model using 15 key variables to predict sodium intake in CKD patients with 71% accuracy.

## Key findings

- The model matched predicted and observed sodium intake categories in 71% of cases.
- The tool was developed using data from 493 patients across six French centers.
- External validation is needed to confirm the model's robustness and generalizability.

## Abstract

Background: Arterial hypertension is highly prevalent among patients with chronic kidney disease (CKD), acting both as a cause and consequence of declining kidney function, and significantly increasing cardiovascular risk. Among modifiable risk factors, diet—particularly excessive sodium intake—plays a central role in the prevention and personalized management of CKD. Methods: This study aimed to develop an innovative, digitally accessible tool to estimate sodium intake in stages 3 to 5 CKD patients, using 24-h urinary sodium excretion as the reference standard. Results: Twenty-five clinical, biological, therapeutic, and dietary variables were collected from 493 patients followed across 6 French centers. A probabilistic Tree-Augmented Naive Bayes model was used to develop the tool based on the 15 most informative variables. The model demonstrated an internal accuracy of 71%, indicating that predicted and observed sodium intake categories matched in 71% of cases. Conclusions: This AI-based prediction model offers a promising clinical tool to estimate daily sodium intake in patients with stages 3 to 5 CKD. However, external validation using independent national and international datasets is essential to establish its robustness and generalizability prior to implementation in routine clinical practice.